When data signals are transmitted over a communication path, various types of distortion and noise are introduced. This is due to interference and the changing and dispersive nature of the communication path. A principal form of distortion is the multipath dispersion which occurs when signals propagate along different or reflected paths through a transmission medium to the receiving destination. For example, in high frequency (2 to 30 MHz) transmissions which are bounced off the ionosphere, multipath dispersion is introduced in the transmission in the form of echoes, time delays, fading, phase changes, and other adverse influences of the communication channel. Accordingly, the signals received are not the same as the original message, and when they are demodulated and decoded there are often errors in the output data. Other adverse effects include interference between the transmitted data symbols (intersymbol interference or ISI), and noise which reduces the signal to noise ratio (SNR) at the receiving end.
Various approaches have been developed to compensate for the adverse effects of the communication channel on data transmissions. One approach, such as disclosed in U.S. Pat. No. 4,058,713 to Di Toro, has been to transmit alternating bursts of a known test signal with segments of the original (unknown) data, and to use the known test signal at the receiver to derive an estimate of the channel influence, which estimate is then employed to process the unknown segments of data using a frequency domain data recovery algorithm. With this type of approach, a delay factor is introduced in the format of the transmitted message, which limits the data rate, and detection errors occur if the channel varies significantly at a rate shorter than the period of the data segment. Another method uses channel response estimates to set the coefficients of adaptive equalizers or recursive filters in a time domain data recovery algorithm. A well known technique called "Adaptive Decision Feedback Equalization" (ADFE) has been developed by Bell Laboratory, as described in "Optimum Mean-Square Decision Feedback Equalization", by J. Salz, BTSJ 52, pp. 1341-1373 (1973), and "A Unified Theory of Data-Aided Equalization", by M. S. Mueller and J. Salz, BTSJ 60, pp. 2023-2039 (1981). The performance obtained by the ADFE technique depends very much on the method used to estimate the channel response, because as the channel response changes, the DFE coefficients must be adapted to compensate for the new channel response. In a slowly varying channel, where the fade rate is much less than 1 Hz, e.g. 0.2 Hz, a simple channel tracking algorithm is usually adequate. The adaptation is usually performed within several update cycles in a time period shorter than the fade rate. However, in a more rapidly fading channel, i.e. with fade rates near 1 Hz and above, the update cycles needed to converge on a new channel response often exceed the fade period, and the updated channel response may be outdated before it can be used. The failure to appropriately track the channel response leads to poor performance of the ADFE technique in a fast fading environment. Also, the requirements for digital data transmissions are more stringent for higher data rates of 1200, 2400, 4800 bps or more. Such data rates require more accurate compensation for dynamic channel variations in the range of 1 Hz or higher for high frequency (HF) transmissions.
A faster channel tracking method requires speeding up the update cycles, typically by using a faster microprocessor or by using an improved updating algorithm which requires less computation. As an example of the former approach, reference is made to U.S. Pat. No. 4,365,338 to McRae et al. This patent discloses the transmission of data in packets made up of successive frames, each having a sequence of N known symbols followed by M unknown data symbols. An estimate of the channel response is updated at the receiving end for each frame. A channel tracking algorithm derives an estimate of the channel response by cross correlating an N+M vector of received symbols with the 2N known symbols of the current and previous frames and the channel estimate for the previous frame. The channel estimates are in the form of N+1 weighting coefficients of a transversal filter function applied to the received symbols. The M unknown symbols in each frame are decoded by a "Data Directed Equalization" (DDE) algorithm which calculates the expected error in the decoding decisions on the unknown symbols, and reiterates the error calculation using refined decoding decisions until final decisions on the M unknown symbols are reached having an acceptably low error factor. However, this type of system has the disadvantage that a heavy load of computation is needed to process and decode the data, requiring a specially designed fast array processor, and only achieves acceptable accuracy by extensive iterative recalculation. A major problem with this approach is the inclusion of the unknown symbols in the cross correlation to solve for the channel estimates. The method estimates the channel response based upon unknown inputs from the transmitter and is limited in accuracy and response time as it requires several frames of data to be iteratively processed before the channel estimates converge on true values.
For some applications, such as defense communications, the requirements for transmission and recovery of data are even more stringent. For example, the length of the transmitted data packet may be shortened or changed, and the transmissions may hop in stepped sequence over different frequencies in order to deter interception. Frequency hopping poses severe requirements on channel tracking, since the multipath dispersion is constantly changing over time and is different from one channel to another. For higher data transmission rates, the heavy computational load of the above-mentioned McRae type of system increases its cost and complexity, deteriorates its real time response, and requires trade-offs in the bit-error rate. The computational load can be lowered using an improved data recovery algorithm, for example, as disclosed in U.S. patent application Ser. No. 694,549 filed Jan. 24, 1985, now U.S. Pat. No. 4,761,796 and entitled "High Frequency Spread Spectrum Communication System Terminal", by J. G. Dunn et al. The Dunn system employs an optimal polyphase code (known symbol) sequence at the beginning of each data packet. The code sequence is matched to the anticipated HF channel characteristics in that it employs two repetitions of the polyphase code, with each repetition being greater in length than the multipath delay. Therefore the second repetition of the code does not have any interference from delayed versions of any preceding unknown data symbols. The modem uses the channel estimates derived from processing the received code sequence to set the tap values for linear canceller, feedback equalizer and matched filter functions in a decoding decision loop. The decision loop provides a non-iterative form of equalization to compensate for the multipath effects of the HF channel. The received unknown data is passed twice (or more) through the decision loop to obtain more accurate symbol decisions on the second pass using the preliminary symbol decisions obtained on the first pass. However, the Dunn system has difficulty tracking the channel at channel fade rates above 1 Hz. This is because the channel response is changing rapidly at these higher fade rates: the channel response is well known for the portion or the unknown data symbols immediately following the training symbols, allowing these data symbols to be demodulated with few bit errors; the channel response typically changes significantly by the end of the packet, resulting in many bit errors when the final data symbols are demodulated.
An extension to the Dunn system (one known sequence and one data sequence per packet) would be to break the packet into shorter frames, and to alternate the known and unknown symbol sequences in each frame so that the framing data would always be located closer to the unknown data, allowing the channel response to be better known during the unknown data. Unfortunately this increases the overhead due to the large amount of training data, so that the available data rate is unacceptably limited.
Another extension to the Dunn system would be to break the packet into shorter frames, and include a header frame using Dunn's training format (2 repetitions of a polyphase code, with each repetition exceeding the multipath delay) and then in the subsequent data frames include known data equal to the multipath delay, ala McRae. This allows a fast and accurate initial channel estimate with subsequent updating to track the changing channel. Unfortunately if low-computational cost (conventional least mean square) updating techniques are used, then this approach can get "lost" midway through the packet if one or the frames fades deeply resulting in many bit errors. Since unknown data is included in the updates of the channel estimate, it is not possible for the tracking mechanism to recover unless expensive computational approaches (ala McRae) are used.